Environmental Impact Assessment Review 30 (2010) 240–246

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Environmental Impact Assessment Review

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A method for the assessment of the visual impact caused by the large-scaledeployment of renewable-energy facilities

Marcos Rodrigues, Carlos Montañés, Norberto Fueyo ⁎Fluid Mechanics Group, University of Zaragoza, María de Luna 3, 50018, Zaragoza, Spain

⁎ Corresponding author. Tel.: +34 976762153; fax: +E-mail address: Norberto.Fueyo@unizar.es (N. Fueyo

0195-9255/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.eiar.2009.10.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 16 July 2009Received in revised form 9 October 2009Accepted 15 October 2009Available online 5 November 2009

Keywords:Visual impactLandscapeVisibilityVisual perceptionRenewable energyGIS

The production of energy from renewable sources requires a significantly larger use of the territorycompared with conventional (fossil and nuclear) sources. For large penetrations of renewable technologies,such as wind power, the overall visual impact at the national level can be substantial, and may prompt publicreaction. This study develops a methodology for the assessment of the visual impact that can be used tomeasure and report the level of impact caused by several renewable technologies (wind farms, solarphotovoltaic plants or solar thermal ones), both at the local and regional (e.g. national) scales. Applicationsare shown to several large-scale, hypothetical scenarios of wind and solar-energy penetration in Spain, andalso to the vicinity of an actual, single wind farm.

34 976761882.).

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1. Introduction

The use of renewable resources for electricity generation is beingactively promoted by most governments in both developed anddeveloping countries. This increase in renewable-energy demand isthe consequence of the need for an increased share of carbon-neutralenergy as an aid in themitigation of climate change, but also of amovetowards a heightened energy independence. These (and other)positive effects of renewable energies are partially offset by someless desirable, unintended features. Among them, this paper addressesthe issue of their visual impact. The footprint of a renewable facility is,for the same installed power, much larger than that for a fossil- ornuclear-fuel one because of the lower energy density of the resourceand of the moderate performance of the capturing and conversionequipment. Individual projects sometimes prompt opposition fromlocal communities (for instance, for off-shore wind farms); forscenarios involving large-scale deployment of renewables in dense-ly-populated regions, such as Europe, the aggregated visual impactmay result in more widespread public-opinion reluctance. It istherefore clear that visual impact on the large-scale should increas-ingly become a factor to contend with in policy scenarios (Ek, 2005;Kaldellis, 2005).

Even for isolated projects, the assessment or quantification of thevisual impact has several inherent difficulties, such as the selection of

landscape components and attributes (visual size, contrast, color,shape, and texture, among others) (Shang and Bishop, 2000), andtheir assimilation with the judgement criteria from the observer.Landscape components can be more easily measured as they arerelated to physical properties; the human appraisal is however mostcomplex, because it depends on their subjective landscape perception,considering the landscape not as a neutral area where processes andfunctions are developed (Boira, 1992), but as a part of their own“living space” (Fremont, 1976) where individuals have their ownperception and relationship with the environment.

Several methodologies have been developed for the visual impactassessment of individual projects, such as Torres et al. (2007),Ladenburg (2007) and Bishop and Miller (2007). Thus, Torres et al.(2007) emphasize the aesthetics aspects in the integration of thewindfarms into the landscape, by using photographs and interviews todevelop an objective indicator. Their approach was later extended toPV solar plants by Torres et al. (2009)with the proposal of an indicator(based on four criteria: visibility, color, fractality and concurrencebetween fixed and mobile panels) for the quantification of theaesthetic impact. Ladenburg (2007) places the focus of the assessmenton the observer's prior experience with a technology, and uses publicsurveys to develop his analysis. Bishop andMiller (2007) highlight theimportance of parameters such as distance, contrast andmotion in thevisual impact assessment, and use photographs, computer simula-tions and interviews in their approach. These methodologies areuseful for the assessment of the visual impact of a single technology(usually wind farms) on the local scale (a single project). Grahamet al. (2009) research, more generally, the aspects that influence the

241M. Rodrigues et al. / Environmental Impact Assessment Review 30 (2010) 240–246

public perception of wind energy and the social reluctance to wind-farm projects, including physical, context, political and socio-economic factors. Higgs et al. (2008) examine the use of IT tools(singularly, Geographical Information Systems and Multi-CriteriaDecision Analysis) to facilitate public participation (particularly atthe local-community level) in the process of wind-farm siting.

In this paper, we propose the use of several indexes to quantify thevisual impact from large-scale scenarios of renewable-energy de-ployment. The indexes thus generated can be used in conjunctionwith other figures of merit as an aid in policy decisions, and allow thecomparison between different scenarios (such as the promotion ofcertain renewable technologies in preference to other ones). Inaddition, links can be established from these quantitative indexes tothe (more subjective) landscape perception by individuals. Themethodology is exemplified for several large-scale-deploymentscenarios in Spain of wind- and thermo-solar power, and also forstudies of the local impact by a single project.

2. Methodology

In this section, the methodology proposed for the assessment ofthe potential visual impact of renewable-energy-deployment scenar-ios is described. The method addresses the impact from both a spatial(fraction of affected territory) and a perceptive (level of detection andrecognition of the facilities) approach. The spatial aspect determineswhether the installed facilities are or are not visible from each of thelocations in the area of study; an algorithm is used to calculate thevisibility taking into account the surface elevation, the dimensions ofthe facilities, the landscape relief and the Earth curvature. The result ofthis process is a visibility map, in which each location is assigned aBoolean value indicating whether any renewable facility is visiblefrom the location. This procedure has the benefits of affording anoverall summary of the visual impact caused, and permitting a firstcomparison among different scenarios using several indexes whichare proposed to this end. The perceptive approach attempts toproceed to quantify the visual perception of the installed facilities. Asecond algorithm is derived from the previous one to assess this visualperception, measuring the fraction of the field of view occupied byrenewable facilities. As a result of this process a visual perceptionmapis obtained, in which each location is assigned the affected view angle.The following subsections describe briefly this methodology, the datanecessary to carry it out and the way in which it has beenimplemented.

2.1. Data and model parameters

The spatial data required for the methodology are: a geo-referenced inventory of the renewable facilities the impact of whichis to be assessed; a Digital Elevation Model (DEM), which in thepresent work is obtained from NASA (2005); a land-use map EEA(2003); a land-cover-height map; and the delineation of severalcommunication networks (typically roads and railroads), obtainedfrom the 2005 transit map by the Spanish Directorate General forTraffic (DGT). The DEM, the land-cover height and the renewable-facilities inventory are used in the visual impact and visual perceptionestimation. The land-cover height includes the natural cover height,obtained from the Spanish Forestry Map issued by the SpanishMinistry for the Environment, as well as the building height, from the2004 Population and Housing Census issued by the Spanish StatisticsInstitute (INE). Since the Spanish Statistical Institute provides thebuilding height as the number of building floors for each Spanishmunicipality, we have assumed that the height of each floor is 3m,and in a simplified way we have used the height of the tallest buildingas the typical height of all the buildings in the municipality urban areafor the purpose of visibility calculations.

The parameters required by the model are: the facility dimensions(height and width); the visual threshold (i.e. the maximum distancefrom which a facility can be recognised); and the height at theobserver position.

2.2. Visual impact

The visual impact calculation consists in determining whether arenewable facility from the inventory can be seen from each of thelocations in the study area, and then aggregating the results in someoverall indexes. Such determination takes into account four factors:the relief (R), the land-cover height (L), the facility height (Ih) andwidth (Iw), and the observer height (O). When a facility is visible froma location, we refer to such location as visually affected. The processfollowed in determining whether a location is visually affected or notis described next.

First, the discernibility range is computed. The discernibility rangeis based on the concept of visual threshold, used in psychophysics anddefined as the minimal object size that can be perceived. Theestimation of the visual threshold is based on Shang and Bishop(2000), who establish that the minimum object size that a personwith a normal visual acuity is able to recognise is 25min2. We haveadopted this concept to establish the maximum distance from whichan object (a renewable-energy facility) can be recognised; this weterm the discernibility range. The discernibility range therefore differsfrom one renewable facility to another, since it depends on the facilitysize; for a given renewable technology t, the relationship between thediscernibility range Δt and the facility dimensions given by theequation: Δt =

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiIwt I

ht c= 25

qwhere c=(180×60/π)2≃1.18 107 is a

constant for the conversion of steradians to square minutes.Next, the pixels i that are occupied by a renewable facility within a

distance Δt from an observation pixel o are identified. View lines aredrawn from the observation point o to the top of the installation atpixel i, and for each intermediate pixel n the height of the line ofvision is compared with the sum of the relief and land-cover heights,Rn+Ln. A value of 1 is assigned to the observation pixel if none of thevisual lines is intercepted by the terrain (Fig. 1), and thus theobservation pixel o is visually affected.

2.3. Visual impact indexes

In order to quantify in a single figure the overall visual impact ofthe large-scale introduction of renewable facilities, the followingindexes are defined:

• The Visually-Affected Area (VA) is defined as the fraction of thesurface area in the analysed region from which a renewable facilitycan be seen:

VA =∑iS

ai

S100 ð1Þ

where Sia is the surface area of the visually-affected pixel i and S is

the total area of the study zone.

• The Visually-Affected Populated Area VP is similarly the fraction ofpopulated area in the study zone that is visually affected; it iscalculated as the ratio between the affected populated area(obtained from EEA (2003)), and the overall extension of urbanarea in the study zone:

VP =∑iS

pai

Sp100 ð2Þ

where Sia is the area of the affected and populated pixel i and Sp is

the total populated area of the study zone.

Fig. 1. Procedure to establish whether an observation pixel o is visually affected.

242 M. Rodrigues et al. / Environmental Impact Assessment Review 30 (2010) 240–246

• The Visually-Affected Travel Time VT is the fraction of travel time inwhich renewable facilities are seen. This index is calculated,separately for road and for railroad traffic, as the ratio betweenthe affected travel time and the total travel time:

VT =∑ttittt

100 ð3Þ

where tit is the travel time of the affected pixel i and tt

t is the totaltravel time. The travel time in a pixel (hours per year) is calculated asthe time taken for a vehicle to cross the pixel times the number of

Table 1Technical parameters of the wind turbine and farm.

Parameter Value

Rated power 2.0 MWRotor diameter 90 mRated wind speed 13 m/sCut-in speed 3.5 m/sCut-out speed 25 m/sHub height 78 mSpacing 8d×8dWake efficiency, ηic 0.84Operating efficiency, ηim 0.98

Fig. 2. Visibility map for a scenario generating 50TW

vehicles per year; the former is estimated as the ratio of the pixellength to the average speed in the road/railroad. Data regarding roadand railroad layout, traffic and average speeds are all taken from the2005 transit map by the Spanish Directorate General for Traffic (DGT).The road network used in the calculation of VT includes motorwaysand the national (trunk) roads; lesser roads (county and local roads)are not included in this index due to the lack of detailed traffic data. Tocalculate the index we have been considered the following vehicles:cars, motorcycles, trucks, buses and vans. For the railways, the datacomprises both the conventional and the high-speed network (datafor 2005).

In addition, the Directly Occupied Area (DOA) is defined as thesum of the areas of those pixels with a renewable facility.

2.4. Visual perception estimation

The visual perception estimation attempts to quantify the visualimpact caused by the introduction in the territory of renewablefacilities. To this end, we have developed an algorithm based on theconcept of visual magnitude (Iverson, 1985). The visual magnitude isdefined as the product of the vertical and horizontal view angles of anobject. For several objects or facilities, we define the visual perceptionindex from a location o as the visual solid angle subtended by all thefacilities present in the analysis area. The process to carry out this

h/year of wind energy (installed power: 32GW).

Table 2Visual impact indexes for six wind-energy penetrations.

Potential DOA(%)

VA

(%)VP VT (%) VT (%)

Twh/y Railroads Roads

23 0.7 10.1 0.8 6.8 10.427 0.8 11.2 0.9 7.8 11.750 1.7 17.2 1.3 9.9 15.4100 3.9 30.3 2.5 17.8 24.9200 9.2 45.2 4.2 28.7 37.8300 15.7 58.4 6.1 44.0 54.5

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calculation is similar to the visibility calculation presented inSection 2.2 above, but rather than assigning a binary value to theobservation pixel o, the perception index Po is calculated as the totalsolid angle subtended divided by 2πsteradians, and expressed as apercentage:

Po = ∑i

At

2πD2oi

100 ð4Þ

where At is the visible area of the facilities (of type t) in pixel i, and Doi

is the distance from the observation point o to the facility at i. In thisequation the visual angle is calculated as the ratio between the visibleinstalled area (determined by the facility size) and the square of thedistance, which has the expected effect of decreasing the perceptionlevel as the observation distance increases. To simplify the calcula-tions and reduce the algorithm execution time, we have consideredthe visible area for a renewable technology t as a rectangle with anarea equal to the device width It

w times the device height Ith: At=ItwIt

h;partial occultation of the base of the facility by the terrain or the land-cover is allowed and accounted for. It should be noted that theperception index is normalized in Eq. 4 with half the full solid angle,which is much larger than the human field of view; thus, Po cannot be

Fig. 3. Visibility map for a scenario generating 53TW

simply interpreted as the fraction of the field of view occupied byrenewable facilities.

2.5. Implementation

The methodology is implemented in a host of specifically-writtenLinux-shell scripts to carry out the visibility calculation and visualperception algorithms, and a Geographical Information System (GIS)to calculate the assessment indexes and visualize the results. Thespatial resolution used is 200 m×200 m for the studies at the nationallevel presented below, and 90 m×90 m for the local ones. The scriptsuse ASCII raster maps as data input and produce the visibility maps inthe same format.

3. Results

In this section we present several scenarios that exemplify theproposed method for visual impact assessment. The first subsectionshows the visibility calculation for several (hypothetical) levels ofelectricity generation from wind and thermo-solar farms in Spain. Inthe second one, an actual renewable facility has been used toexemplify the calculation of the visual perception estimation. As aresult we present a visual perceptionmap, and provide key values thatmay assist in its interpretation. In the proposed scenarios we havechosen wind farms and thermo-solar power stations to demonstratethe proposed methodologies, but these can be easily adapted to otherrenewable technologies.

3.1. Visual impact

This section exemplifies the visibility calculation process throughthe development of several hypothetical scenarios of energy gener-ation with wind farms in Spain. To this end, we have followed thesame methodology as developed by Sanz et al. (2009), which is based

h/year of solar-energy (installed power: 15GW).

Fig. 5. Reference values for the perception index Po as a function of the number of windturbines and the observer distance.

Fig. 4. Perception index Po in the vicinity of a renewable facility.

244 M. Rodrigues et al. / Environmental Impact Assessment Review 30 (2010) 240–246

on a detailed estimation, via simulation, of the wind resource in Spainwith a spatial resolution of 10 km and a temporal resolution of 1h. Thewind data is thereafter employed to estimate the energy producedannually at each pixel in the domain for a given set of wind farmparameters (such as hub height, wind-turbine spacing and powercurve). The wind farm parameters used are those indicated in Table 1.

To reach a certain level of energy generation, the best, most-economical locations are selected among those socially and techni-cally feasible, excluding, for instance, urban areas, protected domainsor abrupt terrain. Specifically, we have analysed six energy productionscenarios with the following production targets: 23TWh/year,27 TWh/year, 50 TWh/year, 100TWh/year, 200TWh/year and300TWh/year. The corresponding installed powers, using a commer-cial, 2 MW turbine, would be respectively: 13, 15, 32, 75, 175 and298GW. For the sake of comparison, the total electricity consumptionin Spain in 2007 was approximately 300TWh/year, and the electricitygeneration from wind turbines in 2007 was 27TWh.

Fig. 2 presents the visibility map obtained for the 50TWh/yearscenario, showing the spatial distribution of the Visually-AffectedArea for such level of wind penetration, and the values of the impactindexes presented above.

In the Table 2 a summary of the visual impact summary ratios forthe six scenarios is presented. The Directly Occupied Area, DOA,ranges between 0.7% for a generation level of 23TWh/year (acontribution corresponding to roughly 8% of the 2007 demand) tonearly 16% for 300TWh/year (a generation level equal 100% of thedemand, disregarding, of course, the lack of simultaneity betweenwind availability and energy demand). The Visually-Affected Area VA

is however much larger, by a factor ranging from 10 (for the lowergeneration levels) to 4 (for the higher ones). The Visually-AffectedPopulated Area is from 0.8% to about 6%. The fraction of travel timeaffected is also notable, from 6.8% for the low-penetration scenario to44% for the large one.

For comparison, Fig. 3 shows the visibility map for a similarscenario of renewable-energy production, but nowwith thermo-solar(central tower) energy. The occupied sites differ from those for wind,since the resource (sun or wind) availability also differs and wechoose in either case the best possible locations among thosetechnically and environmentally feasible. It should also be notedthat, for a similar level of energy generation (53TWh/year in the solarscenario), both the Directly Occupied Area (DOA) and the visually-affected area VA are much smaller than for wind.

3.2. Visual perception measurement

In this section we exemplify the calculation of the perceptionindex. This index can be used to assess the perception impact that a

Fig. 6. Values of the perception index Po for the scenario with 50TWh/y of wind energy.

245M. Rodrigues et al. / Environmental Impact Assessment Review 30 (2010) 240–246

local community would have with the introduction of a renewablefacility in its vicinity, or as an additional figure-of-merit for large-scalestudies. To do so, an actual wind farm, located in the village ofl'Argentera (Tarragona, Spain) has been used; this is a development of6 wind turbines, each with a rated power of 1.65MW. The wind-turbine dimensions are a hub height of 80m, and a blade diameter of70 m. Fig. 4 presents a map with the spatial distribution of visualperception obtained for this scenario. The perception index Po,obtained from Eq. (4) provides a qualitative indication of the impact,with higher indexes representing larger impacts. This mathematical-ly-derived perception index can be related to a human-stanceperception-scale with some additional field work. The work wouldentail the calculation of the perception index for simpler assemblies ofwind turbines from several distances, and then surveying a popula-tion sample to correlate their perceived impact with the impact indexof such installations. As an example, and in order to give somemeaning to the scale in Figs. 4, 5 shows, for reference, some values ofthe perception index and its dependence on the number of turbinesand the distance for the scenario considered. A more structuredapproach is the proposal by Cloquell-Ballester et al. (2006) of amethodology for the validation of indicators, such as the presentperception index.

The analysis can be carried over to large-scale scenarios, such as awhole country. Thus, Fig. 6 shows the geographical distribution of theperception index Po, for a scenario for which 50TWh/year of windenergy are introduced

4. Conclusions and further work

In this work, we have suggested a method for the global assessmentof the visual impact on the landscape of renewable energies, leading to anumber of quantitative indexes. Although best suited for large-scalestudies, it can be used also, as shown, for local projects. The indexes can

be easily related, with some additional work, to the more subjectiveperception of the impact by individuals.

The application to several hypothetical large-scale scenarios in Spainindicate that significant contributions of renewable energies to theelectricity generation entail a significant visual impact, which arguablyshould be considered along with other factors in policy making. Thus,our estimations indicate that for a level of wind-energy penetration of16% of the total electricity generated in 2007, 1.7% of the Spanishterritory would be occupied by renewable facilities, but these would bevisible from 17% of the territory, and during more than 15% of the road-travel time.

The proposed methodology for the estimation of the visual impactallows for quantitative comparisons among several scenarios of energygeneration with renewable technologies. This is particularly usefulwhen working at regional scales, where impact assessment is moredifficult and the proposed indexes can provide an objective and concisebasis for comparison. A further strength of the methodology is itsreliance on standards, and largely published (public) data as modelinputs.

With additional work, further refinements can be incorporatedinto the proposed methodology. For instance, color contrast betweenthe facility and the background can be taken into account; and humansubjectivity can be considered by relating the numerical values of thevisual perception index to acceptability, for instance with the aid ofspecifically developed questionnaires to determine the final level ofvisual impact.

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Norberto Fueyo is a Professor of Fluid Mechanics at the University of Zaragoza. Heobtained his PhD at Imperial College, London, in 1990, under the supervision ofProfessor D.B. Spalding. His research areas are numerical methods, combustion andrenewable energies. He is the author of over 30 papers in indexed scientific journalsand in book chapters.

Marcos Rodrigues is a researcher with the Fluid Mechanics Group at the University ofZaragoza. He obtained his masters degree in Geography and Geographical InformationSystems at the University of Zaragoza in 2006. His research areas are GIS and remotesensing, fire occurrence modelling and renewable energies.

Carlos Montañés is a researcher with the Fluid Mechanics Group at the University ofZaragoza. He obtained his masters degree in Chemical Engineering at the University ofZaragoza in 2005. His research areas are numerical methods, combustion andrenewable energies.